{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import torch\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 10.0)\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "from models import conv_relu, train_model, DEFAULT_DEVICE\n",
    "from dataset import load_dataset\n",
    "from visualization import classes_to_image, high_dimensional_to_rgb\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clear_ticks(ax):\n",
    "    ax.get_xaxis().set_ticks([])\n",
    "    ax.get_yaxis().set_ticks([])\n",
    "\n",
    "def show_example(x, y, prediction=None):\n",
    "    cols = 2 if prediction is None else 3\n",
    "    \n",
    "    plt.subplot(1, cols, 1)\n",
    "    plt.title('Satellite image')\n",
    "    plt.axis('off')\n",
    "    plt.imshow(high_dimensional_to_rgb(np.array(x)))\n",
    "    \n",
    "    ax1 = plt.subplot(1, cols, 2)\n",
    "    plt.title('Ground truth')\n",
    "    clear_ticks(ax1)\n",
    "    plt.imshow(classes_to_image(np.array(y)))\n",
    "\n",
    "    if prediction is not None:\n",
    "        ax2 = plt.subplot(1, 3, 3)\n",
    "        plt.title('Model output')\n",
    "        plt.imshow(classes_to_image(np.array(prediction)))\n",
    "        clear_ticks(ax2)\n",
    "\n",
    "    plt.show()\n",
    "    \n",
    "def to_numpy(a):\n",
    "    return a.detach().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_history(loss_history, train_history, val_history, lr, acc=False, f1=False, kappa=True):\n",
    "    plt.title(f\"Training loss (lr = {lr:.5f}, final loss: {loss_history[-1]:.5f})\")\n",
    "    plt.ylim([0, 1])\n",
    "    plt.plot(loss_history)\n",
    "    plt.show()\n",
    "    \n",
    "    plt.title(f'Evaluation history')\n",
    "    plt.ylim(top=1)\n",
    "\n",
    "    if acc:\n",
    "        plt.plot(train_history.accuracy, 'bo--', label='Train accuracy')\n",
    "        plt.plot(val_history.accuracy, 'go--', label='Val accuracy')\n",
    "        \n",
    "    if f1:\n",
    "        plt.plot(train_history.f1, 'bv--', label='Train F1')\n",
    "        plt.plot(val_history.f1, 'gv--', label='Val F1')\n",
    "        \n",
    "    if kappa:\n",
    "        plt.plot(train_history.kappa, 'bs--', label='Train kappa')\n",
    "        plt.plot(val_history.kappa, 'gs--', label='Val kappa')\n",
    "        \n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load dataset and visualize random examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = load_dataset(\"dataset.hdf5\", normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "EXAMPLES = 10\n",
    "\n",
    "while EXAMPLES > 0:\n",
    "    idx = np.random.randint(0, len(d.train))\n",
    "    (x, y) = d.train[idx]\n",
    "    \n",
    "    if (np.array(y).argmax(axis=0) == 1).sum() == 0:\n",
    "        continue\n",
    "      \n",
    "    show_example(x, y)\n",
    "    EXAMPLES -= 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create and train model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = conv_relu(d.input_bands, 3, n_filters=64, kernel_size=5, layers=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "236/1331 training examples are non-empty\n",
      "67/332 validation examples are non-empty\n",
      "Epoch 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/gpoesia/openafro/models.py:79: RuntimeWarning: invalid value encountered in true_divide\n",
      "  precision = stats.tp / (stats.tp + stats.fp)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 0, loss=2.1672000885009766, prec=[0.943,1.000,0.000]/[0.945,1.000,1.000], recall=[1.000,0.000,0.000]/[1.000,0.000,0.000], acc=0.962/0.964, f1=0.000/0.000, kappa=0.333/0.333, \n",
      "Epoch 2\n",
      "Epoch 3\n",
      "Iteration 16, loss=0.2226828783750534, prec=[0.943,1.000,1.000]/[0.945,1.000,1.000], recall=[1.000,0.000,0.000]/[1.000,0.000,0.000], acc=0.962/0.964, f1=0.000/0.000, kappa=0.333/0.333, \n",
      "Epoch 4\n",
      "Epoch 5\n",
      "Iteration 32, loss=0.20768673717975616, prec=[0.943,1.000,1.000]/[0.945,1.000,1.000], recall=[1.000,0.000,0.000]/[1.000,0.000,0.000], acc=0.962/0.964, f1=0.000/0.000, kappa=0.333/0.333, \n",
      "Epoch 6\n",
      "Epoch 7\n",
      "Epoch 8\n",
      "Iteration 6, loss=0.2436973750591278, prec=[0.943,0.643,1.000]/[0.945,0.000,1.000], recall=[1.000,0.001,0.000]/[1.000,0.000,0.000], acc=0.962/0.963, f1=0.000/0.000, kappa=0.334/0.333, \n",
      "Epoch 9\n",
      "Epoch 10\n",
      "Iteration 22, loss=0.18409956991672516, prec=[0.945,0.819,0.680]/[0.946,0.366,0.000], recall=[1.000,0.036,0.015]/[0.999,0.011,0.000], acc=0.963/0.963, f1=0.061/0.000, kappa=0.374/0.344, \n",
      "Epoch 11\n",
      "Epoch 12\n",
      "Iteration 38, loss=0.157525897026062, prec=[0.953,0.771,0.515]/[0.950,0.374,0.171], recall=[0.996,0.188,0.155]/[0.991,0.097,0.021], acc=0.967/0.961, f1=0.352/0.089, kappa=0.523/0.419, \n",
      "Epoch 13\n",
      "Epoch 14\n",
      "Epoch 15\n",
      "Iteration 12, loss=0.13880962133407593, prec=[0.958,0.830,0.663]/[0.951,0.439,0.226], recall=[0.996,0.277,0.229]/[0.991,0.121,0.037], acc=0.970/0.962, f1=0.471/0.136, kappa=0.596/0.442, \n",
      "Epoch 16\n",
      "Epoch 17\n",
      "Iteration 28, loss=0.08031108975410461, prec=[0.978,0.765,0.758]/[0.956,0.313,0.370], recall=[0.988,0.650,0.469]/[0.972,0.236,0.083], acc=0.979/0.954, f1=0.720/0.247, kappa=0.786/0.486, \n",
      "Epoch 18\n",
      "Epoch 19\n",
      "Epoch 20\n",
      "Iteration 2, loss=0.055701497942209244, prec=[0.985,0.745,0.764]/[0.959,0.293,0.378], recall=[0.985,0.752,0.627]/[0.962,0.296,0.128], acc=0.981/0.950, f1=0.789/0.311, kappa=0.821/0.503, \n",
      "Epoch 21\n",
      "Epoch 22\n",
      "Iteration 18, loss=0.04912915825843811, prec=[0.984,0.897,0.890]/[0.955,0.397,0.472], recall=[0.995,0.743,0.703]/[0.982,0.208,0.180], acc=0.987/0.960, f1=0.854/0.352, kappa=0.868/0.498, \n",
      "Epoch 23\n",
      "Epoch 24\n",
      "Iteration 34, loss=0.03287758305668831, prec=[0.994,0.879,0.874]/[0.959,0.370,0.403], recall=[0.993,0.898,0.891]/[0.973,0.281,0.217], acc=0.991/0.957, f1=0.919/0.389, kappa=0.921/0.525, \n",
      "Epoch 25\n",
      "Epoch 26\n",
      "Epoch 27\n",
      "Iteration 8, loss=0.016344714909791946, prec=[0.997,0.952,0.967]/[0.959,0.455,0.457], recall=[0.997,0.945,0.965]/[0.981,0.276,0.226], acc=0.996/0.962, f1=0.970/0.414, kappa=0.965/0.544, \n",
      "Epoch 28\n",
      "Epoch 29\n",
      "Iteration 24, loss=0.011451413854956627, prec=[0.998,0.969,0.982]/[0.959,0.442,0.468], recall=[0.998,0.962,0.971]/[0.980,0.285,0.226], acc=0.997/0.961, f1=0.980/0.417, kappa=0.977/0.546, \n",
      "Epoch 30\n",
      "Epoch 31\n",
      "Iteration 40, loss=0.012964779511094093, prec=[0.997,0.967,0.973]/[0.959,0.431,0.454], recall=[0.998,0.957,0.970]/[0.979,0.287,0.226], acc=0.997/0.960, f1=0.977/0.414, kappa=0.974/0.543, \n",
      "Epoch 32\n",
      "Epoch 33\n",
      "Epoch 34\n",
      "Iteration 14, loss=0.006588361691683531, prec=[0.999,0.984,0.993]/[0.959,0.447,0.452], recall=[0.999,0.981,0.990]/[0.980,0.285,0.229], acc=0.999/0.961, f1=0.991/0.417, kappa=0.988/0.547, \n",
      "Epoch 35\n",
      "Epoch 36\n",
      "Iteration 30, loss=0.006448963191360235, prec=[0.995,0.988,0.993]/[0.958,0.506,0.508], recall=[0.999,0.922,0.959]/[0.986,0.251,0.199], acc=0.997/0.964, f1=0.975/0.400, kappa=0.969/0.540, \n",
      "Epoch 37\n",
      "Epoch 38\n",
      "Epoch 39\n",
      "Iteration 4, loss=0.012380260042846203, prec=[0.998,0.967,0.963]/[0.960,0.405,0.478], recall=[0.998,0.972,0.977]/[0.977,0.288,0.232], acc=0.998/0.959, f1=0.979/0.417, kappa=0.979/0.539, \n",
      "Epoch 40\n",
      "Epoch 41\n",
      "Iteration 20, loss=0.003957996144890785, prec=[0.999,0.991,1.000]/[0.959,0.450,0.521], recall=[1.000,0.992,0.991]/[0.982,0.269,0.226], acc=0.999/0.962, f1=0.995/0.419, kappa=0.994/0.541, \n",
      "Epoch 42\n",
      "Epoch 43\n",
      "Iteration 36, loss=0.0020271828398108482, prec=[0.999,0.992,0.996]/[0.959,0.450,0.532], recall=[1.000,0.990,0.996]/[0.982,0.274,0.226], acc=0.999/0.962, f1=0.995/0.422, kappa=0.994/0.543, \n",
      "Epoch 44\n",
      "Epoch 45\n",
      "Epoch 46\n",
      "Iteration 10, loss=0.002182732569053769, prec=[1.000,0.992,0.998]/[0.959,0.453,0.529], recall=[1.000,0.993,0.992]/[0.982,0.267,0.226], acc=0.999/0.962, f1=0.996/0.419, kappa=0.995/0.541, \n",
      "Epoch 47\n",
      "Epoch 48\n",
      "Iteration 26, loss=0.0021788212470710278, prec=[1.000,0.991,0.997]/[0.960,0.408,0.536], recall=[0.999,0.992,0.993]/[0.977,0.296,0.226], acc=0.999/0.959, f1=0.995/0.423, kappa=0.994/0.543, \n",
      "Epoch 49\n",
      "Epoch 50\n",
      "Iteration 41, loss=0.0005913811037316918, prec=[0.999,0.994,0.999]/[0.959,0.442,0.510], recall=[1.000,0.992,0.992]/[0.981,0.277,0.226], acc=0.999/0.961, f1=0.996/0.419, kappa=0.995/0.543, \n",
      "lr = 0.0011157142695375642 loss = 0.0005913811037316918\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "EPOCHS = 50\n",
    "lrs = [0.0011157142695375642] # 10**np.random.uniform(-5, -2, 10)\n",
    "\n",
    "points = []\n",
    "\n",
    "for lr in lrs:\n",
    "    loss_history, train_history, val_history, examples = train_model(model, d.train, d.val, lr, epochs=EPOCHS, dtype=torch.float32, batch_size=32, verbose=True)\n",
    "    points.append((lr, loss_history[-1]))\n",
    "    print(\"lr =\", lr, \"loss =\", loss_history[-1])\n",
    "\n",
    "    plot_history(loss_history, train_history, val_history, lr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Learning rate vs final loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7ff815f13780>"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x = np.log10([p[0] for p in points]), y = [p[1] for p in points], )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_history(loss_history, train_history, val_history, lr, acc=True, f1=True, kappa=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AEEAxBQAAEEAxBQAAEEAxBQAAENBzD+iRoNkogba20SO9i0eDz5W9tC9ogfeietzFEVQEsMAtktD0WER7g2/9bW55f2uHmxJapgAAAAIopgAAAAIopgAAAAIopgAAAAIopgAAAAJ6vppvArccqZ1k7a1sRlqcwPrU3kUnappXQNRe5ZEmcSua2jdzdq4GAYCZFr1yr03t79EMXbnXhpYpAACAAIopAACAAIopAACAAIopAACAgJ4D6G2meF+V6nD2KMtYeeuYtvm05utGuBVNbch+EiLBxNZRo8H52Q4rAsCCM+Mh8GmiZQoAACCAYgoAACCAYgoAACCAYgoAACBgBgLotTqCb629Z0cEe+OuDmJPIng/xXBgdfi9cr09yriEIgGgyjTvirGI0TIFAAAQQDEFAAAQQDEFAAAQQDEFAAAQsIAC6F0qg8oT6Wi9cqK12b7a7Hvn9CIrGQwgVuf2K+czzd7cAQAYAS1TAAAAARRTAAAAARRTAAAAARRTAAAAAb0G0Fvjw63B5fqgcXvkumX81pz6BFLpkZ7AF3IvtAt52QFgsZnE7xs60TIFAAAQQDEFAAAQQDEFAAAQQDEFAAAQ0G8P6KE8XMfItdPsKyDdGqiv7R68uhvxKep6w2dtOQFgCeOioF7RMgUAABBAMQUAABBAMQUAABBAMQUAABDQbwC9TXVGrmvA2gR6JP3eFX6vXfhxzzsaLKxcnmDv9OMX7dGXQCYAYPxomQIAAAigmAIAAAigmAIAAAigmAIAAAigmAIAAAjo92q+1oupam+r0jXNPq6o65r3rF2lV6tlPp7EVZFcPQcAWPxomQIAAAigmAIAAAigmAIAAAigmAIAAAjo+XYygbB552AtL9TmnieRXQ/fHqcPgfdspPciErLn1jEAgIWBlikAAIAAiikAAIAAiikAAIAAiikAAICAngPo0bB5i0ieOdj5envv6+PuaX0SPaVHekCPmsR8Ip8DQXUAQAwtUwAAAAEUUwAAAAEUUwAAAAEUUwAAAAG9BtBdG/Zt7Tk7qLec8bhD6aMseCB03Ramn0gova+wOMFyAEA/aJkCAAAIoJgCAAAIoJgCAAAIoJgCAAAImIEe0NuCz3WDPcwLQ9MMhKknkmXuqWfz6nD3BMLm1as4iTd4Eu8vAGi035PWO2Uscl3vzyJ+L2iZAgAACKCYAgAACKCYAgAACKCYAgAACOg3gN6aSZtEL9tjnmbX5Fp7am8L2I27V/RR1M67rVf06Lzb5tNXMHzxBh0BzKBFHK6eU1vYfAm+F7RMAQAABFBMAQAABFBMAQAABFBMAQAABFBMAQAABPR7NV9bwr/1SoDWkUeZUctTLfOpvaCu88qEcV+RV71AY56v6j+Hzisba2/r0zZuX1f4cYsZAGOwBK9W68R7IYmWKQAAgBCKKQAAgACKKQAAgACKKQAAgIB+A+htAeDa7FpbQLpLa9C9dtz62VQPHArZ196SpUvl7W1q17s1LK7RPp95i86DoCTwEL3st8DiR8sUAABAAMUUAABAAMUUAABAAMUUAABAQM8B9J7UhirDPa0Hwputo7YF1Ufohry6d/E2wXB2dS+4kV7Ie/psgFlEWByYWbRMAQAABFBMAQAABFBMAQAABFBMAQAABEw/gF6bR+4MOEdCmcHQ9diz3cEgdvV8WhY8Gm4NvZV9BWsj4XdgRATGgSWDlikAAIAAiikAAIAAiikAAIAAiikAAICAfgPotXnMkXLCUwwQL9jscsuCt67LKAHacYe7R+j5fSG76qrxB5Wre6PHnBZygHy+28Duu493OYAlgpYpAACAAIopAACAAIopAACAAIopAACAgH4D6JFMcddwkaztJAKmiyr7G12ZcXcRv6jeXIzTLIbFuRAAWDJomQIAAAigmAIAAAigmAIAAAigmAIAAAigmAIAAAjo92q+0G1MOq7Wab2LSV9X0YQuJRzbUsyuWbslzLhveTMhXAX2oGlepcfnAKASLVMAAAABFFMAAAABFFMAAAABFFMAAAABPQfQawWDy9MMrVZnnAPh1s5b6/QRsJ7Ee1u73CNchFBr2hnj3XaTrrxyyguxiBEiB9ADWqYAAAACKKYAAAACKKYAAAACKKYAAAACeg6gB5LCnaHr6icD8+6aeeX6hELpLSN3rl4fYfPoPGq3gaXQQzweFgFyAAsALVMAAAABFFMAAAABFFMAAAABFFMAAAABTgQ8gamxfbukm6a9HECxKqW01TQXgH0CM6Zqn6CYAgAACOA0HwAAQADFFAAAQADFFAAAQADFFAAAQADFFAAAQADFFAAAQADFFAAAQADFFAAAQADFFAAAQADFFAAAQADFFAAAQADFFAAAQADFFAAAQADFFAAAQADFFAAAQADFFAAAQADFFAAAQADFFAAAQADFFAAAQADFFAAAQADFFAAAQADFFAAAQMD/AzyfMB+ucS+9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ggGAKAAAggGAKAAAgYMAJ6JEE4D4k4I5cPvJMSDLutNMi2fxNl+1m/4zchxvTJEm9X0h+xyA1vSgjqq5dR9ZLP5lojEwBAAAEEEwBAAAEEEwBAAAEEEwBAAAEDDgBPaIfCYnRO2/XLB8qctSSpgeV8D1q243n6UcyMMm6GLZhXtSBscPIFAAAQADBFAAAQADBFAAAQADBFAAAQMCEB1OueQyoyLr5oo+eC66k53Uc6s7oj112ycnY/X6MGnswD8w8/egTMxVtesaY8GAKAAAghmAKAAAggGAKAAAggGAKAAAgYAbdAX0U1SUD9j/ZsWMK4kzNTWy4G2dwGunwDTMJd5hJs71e90xOZp5k4/S51bXpcdq+GYqRKQAAgACCKQAAgACCKQAAgACCKQAAgIAJT0DvRwJ5r5NtJyCxsG4Th5Pbj37odXLsOCW0R5F4PN74fGcMRqYAAAACCKYAAAACCKYAAAACCKYAAAACJjwBfSYk90UTXkdsGyOb02lZEtgny6CSckct2bxON3UkmRlR0T4xxm2QkSkAAIAAgikAAIAAgikAAIAAgikAAIAAgikAAICACb+abxIM84qk8b1yAxNgmFce9eNKwplwdSJGW12f6KZdRdrgiF8JyMgUAABAAMEUAABAAMEUAABAAMEUAABAAAno6KNIwmsXyYa1SY11iZKx1QADE022Jdkcg9JNW420yxFv04xMAQAABBBMAQAABBBMAQAABBBMAQAABJCAjhEVTTZsujwZ6BhD001gX7Kkt/UAqgZxF/MhJaozMgUAABBAMAUAABBAMAUAABBAMAUAABBAAjpmjH6kFZJ+DgBjpNdJ7g0vymBkCgAAIIBgCgAAIIBgCgAAIIBgCgAAIIBgCgAAIIBgCgAAIIBgCgAAIIBgCgAAIIBgCgAAIIA7oPdc3d1X+3HvbgAAMAoYmQIAAAggmAIAAAggmAIAAAggmAIAAAggmAIAAAjgar6e48o9AAAmCSNTAAAAAQRTAAAAAQRTAAAAAQRTAAAAAQRTAAAAAQRTAAAAAQRTAAAAAQRTAAAAAQRTAAAAAYO9A3pKL3zP3DEcTdW0H0ncdR4AMEyMTAEAAAQQTAEAAAQQTAEAAAQQTAEAAAQMNAE9kSjcE533YqcE7eYlrL+86GdYU2btamgrAICZgZEpAACAAIIpAACAAIIpAACAAIIpAACAgMHeAb02p7jXSdMzWcOE76HeCLzp59WFcL37kSgPAEAzjEwBAAAEEEwBAAAEEEwBAAAEEEwBAAAEDDYBvendr+t4qFnXQT1Osh/UJneTax6qU9OFO83Xh6R4AAAaYmQKAAAggGAKAAAggGAKAAAggGAKAAAggGAKAAAgYMBX89VofBXYTLhqL6ruascutrvXu6ib8ob6X4EmoW0AAEYVI1MAAAABBFMAAAABBFMAAAABBFMAAAABg01Ar0tSnojc4bqNbPivdWr3Tz/+z0swg7x2E/k3LwCA8cfIFAAAQADBFAAAQADBFAAAQADBFAAAQMDw74A+1DtnB/U6oT6cbB7Rhx3sUfzQAADoLUamAAAAAgimAAAAAgimAAAAAgimAAAAApy4SzUwNLYflrR82PUAikUppS2HWQH6BEZMoz5BMAUAABDAaT4AAIAAgikAAIAAgikAAIAAgikAAIAAgikAAIAAgikAAIAAgikAAIAAgikAAIAAgikAAIAAgikAAIAAgikAAIAAgikAAIAAgikAAIAAgikAAIAAgikAAIAAgikAAIAAgikAAIAAgikAAIAAgikAAIAAgikAAIAAgikAAIAAgikAAICA/wdHL98WvAoDUAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x720 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "use_validation_set = True\n",
    "EXAMPLES = 10\n",
    "\n",
    "ds = d.val if use_validation_set else d.train\n",
    "\n",
    "model.eval()\n",
    "\n",
    "with torch.no_grad():\n",
    "    while EXAMPLES > 0:\n",
    "        if use_validation_set:\n",
    "            idx = np.random.randint(0, len(ds))\n",
    "        else:\n",
    "            idx = examples[EXAMPLES - 1]\n",
    "\n",
    "        (x, y) = ds[idx]\n",
    "\n",
    "        if (np.array(y) != 0).sum() == 0:\n",
    "            continue\n",
    "\n",
    "        x = torch.Tensor(x.reshape(1, x.shape[0], x.shape[1], x.shape[2])).to(device=DEFAULT_DEVICE)\n",
    "\n",
    "        prediction = to_numpy(model(x).cpu())[0].argmax(axis=0)\n",
    "\n",
    "        show_example(x[0].cpu(), y, prediction)\n",
    "        EXAMPLES -= 1"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
